dcef47168e1d630fe02634f6a542df0c3571228d,tensorlayer/layers.py,DynamicRNNLayer,__init__,#DynamicRNNLayer#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#,3893

Before Change

        // Creats the cell function
        // cell_instance_fn=lambda: cell_fn(num_units=n_hidden, **cell_init_args) // HanSheng
        self.cell = cell_fn(num_units=n_hidden, **cell_init_args)

        // Apply dropout
        if dropout:
            if type(dropout) in [tuple, list]:
                in_keep_prob = dropout[0]
                out_keep_prob = dropout[1]
            elif isinstance(dropout, float):
                in_keep_prob, out_keep_prob = dropout, dropout
            else:
                raise Exception("Invalid dropout type (must be a 2-D tuple of "
                                "float)")
            try: // TF1.0
                DropoutWrapper_fn = tf.contrib.rnn.DropoutWrapper
            except:
                DropoutWrapper_fn = tf.nn.rnn_cell.DropoutWrapper

            // cell_instance_fn1=cell_instance_fn        // HanSheng
            // cell_instance_fn=DropoutWrapper_fn(
            //                     cell_instance_fn1(),
            //                     input_keep_prob=in_keep_prob,
            //                     output_keep_prob=out_keep_prob)
            self.cell = DropoutWrapper_fn(self.cell,
                      input_keep_prob=in_keep_prob, output_keep_prob=1.0)//out_keep_prob)

        // Apply multiple layers
        if n_layer > 1:
            try:
                MultiRNNCell_fn = tf.contrib.rnn.MultiRNNCell
            except:
                MultiRNNCell_fn = tf.nn.rnn_cell.MultiRNNCell

            // cell_instance_fn2=cell_instance_fn // HanSheng
            try:
                // cell_instance_fn=lambda: MultiRNNCell_fn([cell_instance_fn2() for _ in range(n_layer)], state_is_tuple=True) // HanSheng
                self.cell = MultiRNNCell_fn([self.cell] * n_layer, state_is_tuple=True)
            except: // when GRU
                // cell_instance_fn=lambda: MultiRNNCell_fn([cell_instance_fn2() for _ in range(n_layer)]) // HanSheng
                self.cell = MultiRNNCell_fn([self.cell] * n_layer)

After Change

                self.cell = MultiRNNCell_fn([cell_creator() for _ in range(n_layer)], state_is_tuple=True)
            except: // when GRU
                // cell_instance_fn=lambda: MultiRNNCell_fn([cell_instance_fn2() for _ in range(n_layer)]) // HanSheng
                self.cell = MultiRNNCell_fn([cell_creator() for _ in range(n_layer)])

        if dropout:
            self.cell = DropoutWrapper_fn(self.cell,

Instances